The growing interest from telecommunication services providers to offer wireless based services has spurred an also growing demand for wireless spectrum. This has made the tasks related to spectrum management more complicated, especially those related to the allocation of spectrum between competing uses and users. Economically efficient spectrum allocation and assignment requires up to date information on the value of spectrum. Consequently, many spectrum management authorities are or have been elaborating regulations in order to increase the use of market based mechanisms for spectrum management, thus reducing their emphasis on command and control methods.
Spectrum trading (ST) is a market based mechanism where buyers and sellers determine the assignment of spectrum and its uses. That is, it can address both the allocation and assignment aspects of spectrum use. The assignment of spectrum licenses through spectrum trading markets can be used as a mechanism to grant access to spectrum to those who value it most and can use it more efficiently. For it to be optimally effective, a secondary market must exist that allows spectrum users to optimally choose between capital investment and spectrum use on a continuous basis, not just at the time of initial assignment.
This research identifies the different technical architectures for ST markets and studies the possible behaviors and interactions in spectrum trading markets with the use of Agent based Computational Economics (ACE). The research objective is to understand and determine the conditions that lead to viable spectrum trading markets. This analysis is valuable because it can help regulators prepare for plausible future scenarios and create policy instruments that promote these markets. It is also of value to wireless service providers as they can use the results of this work to understand the economic behavior of different ST market implementations and prepare strategies to participate in these markets.
